Dataset Information

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding.

ABSTRACT:

Purpose

In radial imaging, projections may become "miscentered" due to gradient errors such as delays and eddy currents. These errors may result in image artifacts and can disrupt the reliability of direct current (DC) navigation. The proposed parallel imaging-based technique retrospectively estimates trajectory error from miscentered radial data without extra acquisitions, hardware, or sequence modification.

Theory and methods

After phase correction, self-calibrated GRAPPA operator gridding (GROG) weights are iteratively applied to shift-miscentered projections toward the center of k-space. A search algorithm identifies the shift that aligns the peak k-space signals by maximizing the sum-of-squares DC signal estimate of each projection. The algorithm returns a trajectory estimate and a corrected radial k-space signal.

Results

Data from a spherical phantom, the head, and the heart demonstrate that image reconstruction with the estimated trajectory restores image quality and reduces artifacts such as streaks and signal voids. The DC signal level is increased and variability is reduced.

Conclusion

Retrospective phase correction and iterative application of GROG can be used to successfully estimate the trajectory error in two-dimensional radial acquisitions for improved image reconstruction without requiring extra data acquisition or sequence modification.

SUBMITTER: Deshmane A

PROVIDER: S-EPMC4567526 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding.

Deshmane Anagha A Blaimer Martin M Breuer Felix F Jakob Peter P Duerk Jeffrey J Seiberlich Nicole N Griswold Mark M

Magnetic resonance in medicine 20150311 2

<h4>Purpose</h4>In radial imaging, projections may become "miscentered" due to gradient errors such as delays and eddy currents. These errors may result in image artifacts and can disrupt the reliability of direct current (DC) navigation. The proposed parallel imaging-based technique retrospectively estimates trajectory error from miscentered radial data without extra acquisitions, hardware, or sequence modification.<h4>Theory and methods</h4>After phase correction, self-calibrated GRAPPA operat ...[more]

PMID: 25765372

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Project description:BackgroundT2 * mapping can characterize tumor hypoxia, which may be associated with resistance to therapy. Acquiring T2 * maps during MR-guided radiotherapy could inform treatment adaptation by, for example, escalating the dose to resistant sub-volumes.PurposeThe purpose of this work is to demonstrate the feasibility of the accelerated T2 * mapping technique using model-based image reconstruction with integrated trajectory auto-correction (TrACR) for MR-guided radiotherapy on an MR-Linear accelerator (MR-Linac).Materials and methodsThe proposed method was validated in a numerical phantom, where two T2 * mapping approaches (sequential and joint) were compared for different noise levels (0,0.1,0.5,1) and gradient delays ([1, -1] and [1, -2] in units of dwell time for x- and y-axis, respectively). Fully sampled k-space was retrospectively undersampled using two different undersampling patterns. Root mean square errors (RMSEs) were calculated between reconstructed T2 * maps and ground truth. In vivo data was acquired twice weekly in one prostate and one head and neck cancer patient undergoing treatment on a 1.5 T MR-Linac. Data were retrospectively undersampled and T2 * maps reconstructed, with and without trajectory corrections were compared.ResultsNumerical simulations demonstrated that, for all noise levels, T2 * maps reconstructed with a joint approach demonstrated less error compared to an uncorrected and sequential approach. For a noise level of 0.1, uniform undersampling and gradient delay [1, -1] (in units of dwell time for x- and y-axis, respectively), RMSEs for sequential and joint approaches were 13.01 and 9.32 ms, respectively, which reduced to 10.92 and 5.89 ms for a gradient delay of [1, 2]. Similarly, for alternate undersampling and gradient delay [1, -1], RMSEs for sequential and joint approaches were 9.80 and 8.90 ms, respectively, which reduced to 9.10 and 5.40 ms for gradient delay [1, 2]. For in vivo data, T2 * maps reconstructed with our proposed approach resulted in less artifacts and improved visual appearance compared to the uncorrected approach. For both prostate and head and neck cancer patients, T2 * maps reconstructed from different treatment fractions showed changes within the planning target volume (PTV).ConclusionUsing the proposed approach, a retrospective data-driven gradient delay correction can be performed, which is particularly relevant for hybrid devices, where full information on the machine configuration is not available for image reconstruction. T2 * maps were acquired in under 5 min and can be integrated into MR-guided radiotherapy treatment workflows, which minimizes patient burden and leaves time for additional imaging for online adaptive radiotherapy on an MR-Linac.

Project description:Purpose To develop and evaluate a retrospective method to minimize motion artifacts in structural MRI. Materials and Methods The motion-correction strategy was developed for three-dimensional radial data collection and demonstrated with MPnRAGE, a technique that acquires high-resolution volumetric magnetization-prepared rapid gradient-echo, or MPRAGE, images with multiple tissue contrasts. Forty-four pediatric participants (32 with autism spectrum disorder [mean age ± standard deviation, 13 years ± 3] and 12 age-matched control participants [mean age, 12 years ± 3]) were imaged without sedation. Images with and images without retrospective motion correction were scored by using a Likert scale (0-4 for unusable to excellent) by two experienced neuroradiologists. The Tenengrad metric (a reference-free measure of image sharpness) and statistical analyses were performed to determine the effects of performing retrospective motion correction. Results MPnRAGE T1-weighted images with retrospective motion correction were all judged to have good or excellent quality. In some cases, retrospective motion correction improved the image quality from unusable (Likert score of 0) to good (Likert score of 3). Overall, motion correction improved mean Likert scores from 3.0 to 3.8 and reduced standard deviations from 1.1 to 0.4. Image quality was significantly improved with motion correction (Mann-Whitney U test; P < .001). Intraclass correlation coefficients for absolute agreement of Tenengrad scores with reviewers 1 and 2 were 0.92 and 0.88 (P < .0005 for both), respectively. In no cases did the retrospective motion correction induce severe image degradation. Conclusion Retrospective motion correction of MPnRAGE data were shown to be highly effective for consistently improving image quality of T1-weighted MRI in unsedated pediatric participants, while also enabling multiple tissue contrasts to be reconstructed for structural analysis. © RSNA, 2018 Online supplemental material is available for this article.

Dataset Information

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding.

Purpose

Theory and methods

Results

Conclusion

Publications

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding.

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